Unfortunately, many of the sites above to do not show all of the count data available in a given city or region. For example, the Boulder site only shows short-duration bicycle and pedestrian counts collected as part of the regularly motor vehicle counting program, and does not provide access to data from their many permanent bicycle count stations.

For this reason, more efforts on creating centralized data clearinghouses for bicycle and pedestrian count data are needed. To that end, the National Institute of Transportation and Communities (NITC) has assembled a pooled-fund of local, regional, state and federal agencies, which will create an online national data archive for non-motorized traffic count data. Work on the archive is scheduled to start in March 2014. For more information, contact Hau Hagedorn.

Turner and Lasley discuss QA for non-motorized counts and give an example of how they cleaned data from an infrared bicycle and pedestrian counter in their 2013 paper.

The following document details how to compute AADBP according to the AASHTO method in order to create traffic pattern plots.

Miranda-Moreno and others have created a statistically-based method for grouping bicycling patterns using data from 40 North American locations. They classified these locations into four groups: utilitarian, mixed utilitarian, recreational and mixed recreational.

In 2013, Colorado Department of Transportation released a report detailing the process of inventorying, grouping, and computing seasonal adjustment factors for the state. Locations are classified into three groups: mountain non-commute, urban plains non-commute and commute.

A 2012 Colorado Department of Transportation report discusses the topic of grouping sites and more generally recommends three basic groups: commute, non-commute and mixed.

Cluster analysis offers a more statistically based option to grouping locations based on daily and monthly factors. For motor vehicles, the Traffic Monitoring Guide Section 3.2 discusses the pros and cons of cluster analysis compared to other methods for grouping stations, and Appendix G gives an example of cluster analysis applied to North Carolina motor vehicle data. For non-motorized traffic, the 2013 report for the Colorado Department of Transportation (page 95) includes an example of how cluster analysis was applied to bicycle and pedestrian count data.

Alex Hyde-Wright has created an example of a simple method for estimating factors for one permanent count station. This example can be downloaded.

If no permanent bicycle or pedestrian count data are available in your region or state, the National Bicycle and Pedestrian Documentation Project, a joint effort by Alta Planning and Design and the Institute of Transportation Engineers, provides a set of factors that can be downloaded from their website (see the Extrapolation Workbook). Since bicycle and pedestrian traffic patterns vary greatly by geography and climate, applying these national factors can result in large error and may only be appropriate for very rough estimates.

El Esawey and others, working with data from Vancouver, British Columbia, have investigated the details of how to estimate hourly, daily and monthly factors, including investigating how to include weather factors. Their first paper discusses the best approaches to computing daily factors specifically. Their second paper analyzes both daily and monthly factors and can be found below in addition to their TRB presentation:

Boulder County, Colorado conducts a short duration bicycle counting program as part of its motor vehicle count program using pneumatic tube counters. Through extensive testing using various equipment configurations, the county determined that bicycles were being counted as trucks. To improve the accuracy of the off-the-shelf pneumatic tube counter, the county modified the counter’s vehicle classification scheme so that fewer cyclists were misclassified.3 Below is a presentation from Alex Hyde-Wright and Brian Graham of Boulder County as well as instructions for the classification scheme and a copy of the classification scheme.